Hello, which part, specifically, are you finding it difficult to follow? I took a closer look myself and can deduce the following rough steps to help you get started:

Step 1 - filtering

• Filter out cells with fewer than 400 expressed genes
• Filter include highly variable genes across all tissues (you can use your own metrics, if you wish)

Step 2 - ICA (independent component analysis)

• Convert highly variable gene matrices to Z-scores ("[The] selected genes were then centered and scaled across all cells")
• Perform ICA using fastICA package in R, configured to output the first 60 components, and performed separately on each tissue.

Step 3 - KNN clustering

Perform clustering on the 60 ICA components using the cluster implementation in Seurat. Basically, re-use Seurat's functions FindNeighbors() and FindClusters(). I use these in a function in a package that I'm currently developing, to give you an idea: https://github.com/kevinblighe/scToolkit/blob/master/R/clusKNN.R

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That should bring you up to the line "To identify finer substructure among these classes, classes with more than 200 cells were selected for subclustering", whereby they then commence a second round of ICA on a finer subset of genes, it seems.

Unfortunately, following bioinformatics methods can be a nightmare, because it is impossible to accurately write in English language the minute details that are required to comprise a comprehensive methodology.